1. Field of the Invention
This invention relates to presence detection systems using reflected wave energy, more particularly to such systems distinguishing a target from clutter by analyzing frequency components of returned energy.
2. Description of the Related Art
It is well-known in signal processing, as for radar, to analyze a plurality of frequency components of reflected wave energy to detect a target. Typically, these frequency components each correspond to a target having a predetermined relative velocity and thus serve to distinguish a target from clutter returns from the sea surface, chaff, and the like having a different relative velocity. However and insofar as the applicants are aware, it is not known to analyze a plurality of the frequency components together and over time to detect predetermined characteristics of the reflected energy associated with a target.
It is well-known to separate the individual frequency components by a plurality of analog or digital "filters" which, for the purposes of the present application, may be defined as providing such components which are continuous analog, bipolar signals at each frequency. It is also well-known to separate the frequency components by a discrete Fourier transform wherein the components represent the relative amplitude of the energy at each frequency. In digital signal processing, well-known methods provide for each desired frequency component a plurality of filter or Fourier coefficients effective at a predetermined sample rate of the reflected energy. The vast number of arithmetic operations required for these methods typically require relatively large processors that are not adapted for use in small vehicles and, although precise, are too slow for certain applications. In radar fuzing for example, a vehicle in the order of 30 centimeters in diameter may be required to detect in milliseconds a target not more than 30 meters away moving at a relative velocity of 2000 meters per second in the presence of chaff and time-varying clutter as from a sea surface as close as the target.
Parallel processors perform vast numbers of arithmetic operations in a relatively short time. Typically, however, such processors are relatively large and complex to program. The development of artificial neural network topology embodied in a single chip is believed to offer an alternative to conventional parallel processors. However, artificial neural network developments are usually focused on "learning" rather than on parallel processing itself so that, insofar as known to the applicants, the prior art does not include specific arrangements using neural network topology for spectral analysis of reflected wave energy.
For the purposes of the present invention, artificial neural network topology may be defined as the topology of a network receptive to a plurality of input signals and generating a plurality of output signals where the network has the following three characteristics: First, a plurality of multiplier elements each corresponding to one of the input signals and to one of the output signals and generating a product signal representing the product of the amplitude of the input signal and a selectable factor individual to the multiplier element. Second, a plurality of summing elements corresponding to one of the output signals and generating a sum signal representing the sum of the product signals corresponding to the output signal. Third, a plurality of activation elements each corresponding to one of the output signals, receiving the sum signal corresponding to the output signal, and generating the output signal in accordance with a selectable activation function of the sum signal. Typically, the activation function used with an artificial neural network is generally sigmoidal or S-shaped with the sum signal being represented by the X coordinates and the output signal being represented by the Y coordinates, the function having a central portion and two asymptotic portions extending along the X axis oppositely of the central portion.
It is known to provide an artificial neural chip having the three above-identified characteristics where the selectablity of the activation function involves the slope of the central portion, the spacing of the asymptotic portions along the Y axis, and the position of the sigmoid in relation to the X axis. When the slope is relatively small and the asymptotic portions relatively closely spaced, the resulting sigmoid approximates a logarithmic curve. Insofar as known to the applicants, the prior art does not include the use of such a selectable sigmoid for spectral analysis.
However, it will be apparent that the existence of compact, fast parallel processors using artificial neural network or other topology together with arrangements for isolating time-varying frequency components in reflected wave-energy, however effective and novel, cannot distinguish between a target and clutter in the absence of specific criteria characterizing such components in energy reflected from the target in relation to those from the clutter.